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Floodplains display exceptional variation in habitat type, connectivity, and vegetation structure that make them ideal landscapes in which to address biophysical controls on primary production. However, our ability to do so requires fine-scale assessment of biophysical complexity over large spatial gradients in habitat heterogeneity, species composition, and productivity. We used LiDAR data and hydrologic modeling to quantify surface elevation, hydrologic connectivity, and a vegetation structural diversity index (VSDI) in 551 patches across a floodplain forest of a montane river corridor. We also estimated terrestrial primary production via the normalized difference vegetation index (NDVI) in each floodplain patch. Our main goal was to compare abiotic and biotic controls on terrestrial primary production using a path analysis model to estimate direct and indirect effects on NDVI values. Across the floodplain, patch inundation was predominantly low and negatively related to mean patch elevation (r = −0.434, p < 0.001) and distance to the river channel (r = −0.397, p < 0.001). Most patches exhibited high VSDI, corresponding to a total canopy cover of 25–65% and an average canopy height of 8.5 m. Path analysis revealed direct effects of inundation on canopy cover and NDVI, indicating abiotic control on both floodplain vegetation distribution and productivity. Canopy cover mediated indirect effects of inundation on vegetation structural diversity, which was in turn a strong mediator of the effects of canopy cover on forest productivity. Our results suggest that coexisting layers of vegetation in a floodplain patch provide complementary functional traits that interact with flooding regime to collectively increase aboveground productivity. [ABSTRACT FROM AUTHOR]

Because many vessels use the Automatic Identification System (AIS) to broadcast GPS positions, recent advances in satellite technology have enabled us to map global fishing activity. Understanding of human activity at sea, however, is limited because an unknown number of vessels do not broadcast AIS. Those vessels can be detected by satellite-based Synthetic Aperture Radar (SAR) imagery, but this technology has not yet been deployed at scale to estimate the size of fleets in the open ocean. Here we combine SAR and AIS for large-scale open ocean monitoring, developing methods to match vessels with AIS to vessels detected with SAR and estimate the number of non-broadcasting vessels. We reveal that, between September 2019 and January 2020, non-broadcasting vessels accounted for about 35% of the longline activity north of Madagascar and 10% of activity near French Polynesia and Kiribati's Line Islands. We further demonstrate that this method could monitor half of the global longline activity with about 70 SAR images per week, allowing us to track human activity across the oceans. [ABSTRACT FROM AUTHOR]